Citation and policy based document classification

ABSTRACT

Disclosed herein are system, method, and computer program product embodiments for rapid identification and access to relevant regulatory documents. A data model relating regulatory mandates and requirements to citations appearing within an enforcement document is used to rapidly access specific citations within an enforcement document. In the case of image-based enforcement documents, the originality of these documents is preserved while allowing a user to see where the relevant citations appear in the document images. The relevant citations are further compared to business policies to identify potential impacts of regulatory mandates and requirements.

BACKGROUND

For companies operating in highly-regulated industries, it is critical to stay on top of new regulations and regulatory enforcement actions. In order to do this, companies often hire outside legal vendors to provide updates on regulations, typically by manually tracking changes to the regulatory landscape. Legal advisors performing this manual tracking often need to identify regulatory citations across many documents from a variety of sources, such as agency websites and news outlets.

These documents may themselves be large and densely-packed with information. Legal advisors faced with unpacking the regulatory landscape by reviewing these documents are tasked with identifying which document is relevant, and which portions of relevant documents are themselves relevant.

For this reason, it is desirable to provide approaches to simplify identification of and access to relevant regulatory documents.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are incorporated herein and form a part of the specification.

FIG. 1 is a flowchart illustrating steps by which relevant regulatory documents are identified and made available, in accordance with an embodiment.

FIG. 2 illustrates an architecture for identification and presentation of relevant regulatory documents, in accordance with an embodiment.

FIG. 3 illustrates components of enforcement and document processors, in accordance with an embodiment.

FIG. 4 illustrates an exemplary image-based document highlight implementation 400, in accordance with an embodiment.

FIG. 5 is an exemplary relationship structure for associating citations in enforcement documents with regulatory requirements and mandates, in accordance with an embodiment.

FIG. 6 illustrates an exemplary user interface (UI), in accordance with an embodiment.

FIG. 7 illustrates an exemplary UI with relevant information highlights, in accordance with an embodiment.

FIG. 8 is a flowchart illustrating a navigation flow, in accordance with an embodiment.

FIG. 9 is a flowchart illustrating an alternate navigation flow, in accordance with an embodiment.

FIG. 10 is an example computer system useful for implementing various embodiments.

FIG. 11 illustrates a block diagram of a system for analyzing documents, according to some embodiments.

FIG. 12 is a flowchart illustrating a method 1200 for processing documents, according to some embodiments.

In the drawings, like reference numbers generally indicate identical or similar elements. Additionally, generally, the left-most digit(s) of a reference number identifies the drawing in which the reference number first appears.

DETAILED DESCRIPTION

Businesses in many industries need to deal with emerging risks of possible enforcement actions. For example, businesses in the heavily-regulated banking industry must be aware of possible regulatory violations they may have committed, and the possible risks from those violations based on the enforcement actions. Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for rapid identification and access to relevant regulatory documents.

Presently, businesses handle the identification of enforcement actions mostly manually. For example, such businesses may manually log possible regulatory violations for follow up. Separately, such businesses may subscribe to various publications that provide updates on regulatory actions, such as enforcement actions, relevant to the operation of those businesses. Sources of enforcement actions, such as news publications, aggregators, and government websites, by way of non-limiting example, may supply updates or digests of updates on the various enforcement actions, and need to be visited manually reviewed manually to determine the relationship to possible regulatory violations. Not only is this process time consuming, but also prone to error, such as a failure to identify and properly appraise a risk.

FIG. 1 is a flowchart 100 illustrating steps by which relevant regulatory documents are identified and made available, in accordance with an embodiment. A number of sources of regulatory documents, such as publications that provide updates on regulatory actions, are scraped at step 102 to locate an enforcement document. An enforcement document is, by way of non-limiting example, a document detailing a particular enforcement action taken by a government agency. Some non-limiting examples of enforcement documents include cease and desist orders, consent orders, formal agreements, securities enforcement actions, and complaints.

At step 104, pattern recognition is performed on the enforcement document in order to identify a citation within the enforcement document. For example, optical character recognition (OCR) may be performed on the enforcement document if the enforcement document is stored as an image or a series of images. The text version of the enforcement document generated by the OCR process may be searched in order to identify a citation (e.g., a regulatory citation, such as “37 C.F.R. § 1.97”). One skilled in the relevant arts will appreciate that the citation may be identified through a number of approaches, including regular expressions (given the typically standard format of a citation) or a text search for a specific citation. With the citation identified, a relationship is established between the citation and one or more regulatory requirements at step 106, in accordance with an embodiment.

Identifying citations can be challenging due to various factors. For example, OCR often results in textual artifacts that can distort a citation, such as replacing a letter with a number or adding spaces between characters. The pattern recognition needs to be able to identify citations even if there is a problem with the OCR, or at least identify potential citations for manual review. As another example, citations can be expressed over a range of sections or subsections, which may result in a format that is unexpected for a single citation. The pattern recognition needs to identify this and adjust accordingly. As still another example, citations can vary in length, such as citing to a section in one instance and a subsection in another. They can also extend across lines, hiding the end of the citation. The pattern recognition needs to be able to identify both the beginning and the end of the citation.

FIG. 2 illustrates an architecture 200 for identification and presentation of relevant regulatory documents, in accordance with an embodiment. The architecture 200 is implemented as a software architecture executing on computer hardware. Architecture 200 may be disposed across multiple software components, such as individual modules, applications, or services, or may be implemented in a monolithic architecture. Architecture 200, or components thereof, may also be implemented on a single or multiple physical computing devices, including local or remote computing devices. Processes described herein need not be performed strictly within a specific software component of architecture 200 of FIG. 2, and any such relationship described herein is provided by way of non-limiting example. These implementations are provided by way of example, and not limitation, and one skilled in the relevant arts would appreciate that other implementations are contemplated within the scope of this disclosure.

Architecture 200 includes a scraper 202 that provides access to websites and other document sources of enforcement information, in accordance with an embodiment. By way of non-limiting example, document sources may include services provided by the Department of Justice, Consumer Financial Protection Bureau, Federal Trade Commission, Federal Deposit Insurance Corporation, Financial Crimes Enforcement Network, Federal Reserve Board, Office of the Comptroller of the Currency, and the U.S. Department of the Treasury on which enforcement documents are held. One skilled in the relevant arts will appreciate that any document source is usable, including but not limited to local, state, federal and private agency servers.

Each source of scraped enforcement information may be arranged in a variety of different ways, and may not itself provide an interface for directly accessing relevant information to be accessed. Accordingly, scraper 202 may include a scraping interface, in accordance with an embodiment, that provides the ability to read text information from specific sources. By way of non-limiting example, the scraping interface may look for fields within a markup language document that contain relevant text.

In accordance with an embodiment, the enforcement documents are routed through an enforcement processor 204 and a document processor 206. These processors parse the enforcement documents in various ways, detailed further herein. In particular, the enforcement processor 204 processes the enforcement documents for the purpose of parsing out information regarding enforcement actions contained in the enforcement documents, in accordance with an embodiment. The document processor 206 aids in this task by performing document-level manipulations to access the contents of the enforcement documents.

The results of the enforcement processor 204 and document processor 206 are held in storage 208, in accordance with an embodiment. Storage 208 may be, by way of non-limiting example, a database. A user wishing to access information about the enforcement actions may connect via web access 214 or other appropriate service as will be readily appreciated by one skilled in the relevant art. Web access 214 serves as a front end to enforcement service 210 and document service 212, which provide the information regarding enforcement actions from storage 208 that was stored there by enforcement processor 204 and document processor 206.

FIG. 3 illustrates components of the enforcement and document processors in an architecture 300, in accordance with an embodiment. As before, enforcement processor 304 and document processor 306 function together to read and interpret enforcement documents. One skilled in the relevant arts will appreciate that not all of the components illustrated in architecture 300 are required in every instance, and may not be present in every implementation.

When an enforcement document is retrieved (e.g., via scraping) it is processed by enforcement processor 304 and document processor 306 for a variety of purposes. If the document is in a text-based format, then it can be natively handled by enforcement processor 304 to identify specific elements of the enforcement document.

Pattern recognition component 304 a is configured to identify regulatory citations in an enforcement document, in accordance with an embodiment. One skilled in the relevant art will appreciate that other patterns may be identified in an enforcement document for association with a regulatory requirement, such as statutory citations, case law citations, and so forth. In accordance with an embodiment, the recognized pattern has a regular form. In the case of a regulatory citation, this is [TITLE] “C.F.R. §” [SECTION], and some variants thereof. Text of such regular form can be identified using regular expressions (regex). With regular expressions, all regulatory citations within an enforcement document can be found, and the appearance of those particular regulatory citations provides information about the enforcement actions taken in the enforcement document.

Phrase extraction component 304 b is configured to perform phrase extraction of the enforcement document, in accordance with an embodiment. Phrase extraction may be performed as described in co-pending U.S. patent application Ser. No. 16/536,645, entitled “SEARCH PATTERN SUGGESTIONS FOR LARGE DATASETS”, incorporated herein by reference in its entirety, although one skilled in the relevant art will appreciate that other approaches may be used.

Summarization component 304 c is configured to generate a summary of the enforcement document, in accordance with an embodiment. For example, summarization component 304 c may use extracted phrases from phrase extraction component 304 b to summarize the enforcement document, although one skilled in the relevant arts will appreciate that other approaches are contemplated within the scope of this disclosure.

Document tagging component 304 d is configured to classify the enforcement document according to requirements and mandates associated with enforcement actions within the enforcement document, in accordance with an embodiment. Classification and tagging of enforcement documents may be performed through a variety of mechanisms, including classification and tagging based on requirements and mandates associated with citations appearing in the enforcement document, keyword-based classification, or manual classification. However, in accordance with an embodiment, classification is performed in accordance with document classification approaches discussed in co-pending U.S. patent application Ser. No. 16/536,645, entitled “SEARCH PATTERN SUGGESTIONS FOR LARGE DATASETS”, incorporated herein by reference in its entirety. For example, phrase-based scoring, in conjunction with phrase extraction component 304 b, may be employed against a full set of enforcement documents in order to determine a classification and tags for scraped enforcement documents.

Each of the components of enforcement processor 304 operates on text-based enforcement documents, in accordance with an embodiment. However, in the event that an enforcement document is image-based, document processor 306 is used to provide the needed text to enforcement processor 304. Document processor 306 also provides relevant document information for output via web access 214, supporting document service 212, as illustrated in FIG. 2.

By way of non-limiting example, an image-based document, such as an enforcement document, may be stored as an image-based Portable Document Format (PDF) file, as individual image files (e.g., JPEG, TIFF, or PNG), or in some other archive as would be understood by one skilled in the relevant arts. In accordance with an embodiment, document processor 306 performs any processing alongside the enforcement document, and does not directly manipulate the enforcement document. The results of this processing are stored in storage 208 as illustrated in FIG. 2.

While a PDF document can be modified to include corresponding text along with an image of the text, in some embodiments the enforcement document must remain original. By maintaining the enforcement document intact and without modification, a user may feel comfortable that the document is exactly as it was prepared by the agency issuing it. In addition, the enforcement document can be streamed to a user directly from the source, further limiting the ability to modify its contents.

In order to interpret the contents of an image-based enforcement document, image to data component 306 a extracts data from the images, in accordance with an embodiment. For example, image to data component 306 a may perform OCR on the images in order to extract a text-based version of the document (usable by enforcement processor 304). In the case of an image-based PDF document, the individual pages of the PDF document are converted into individual image-based files for processing, in accordance with an embodiment.

While the text-based version of the enforcement document is usable for the tasks required by enforcement processor 304, it may be desirable to show relevant portions of the original enforcement document itself.

In accordance with an embodiment, the OpenCV library for Python may be used to operate on the image-based data of the original enforcement document. Computer vision component 306 b is configured to look for certain shapes in the image-based enforcement document, in accordance with an embodiment. These shapes can be used later for finding and matching text based on the occurrence of certain shapes that correspond to words or phrases within the image-based document.

Find text component 306 c is configured to match text with the shapes identified by computer vision component 306 b. In accordance with an embodiment, an image-to-data conversion is performed on the image-based document (e.g., using the Python library pytessaract, which has an image_to_data function). Input text (such as a word or phrase to search for) is identified in the output data, and pixel positions corresponding to the position in the output data are obtained from the converted data. In an embodiment, the left and top coordinates, and a width and height, corresponding to the pixel positions in the image are collected for identifying the relevant portion of text in the image-based document, although one skilled in the relevant arts will appreciate that other implementations are possible within the scope of this disclosure.

Highlight border component 306 d is configured to highlight the shapes corresponding to the matched text within the original image-based enforcement document. Highlight border component 306 d may use the coordinate and size data from find text component 306 c to overlay a duplicate image over the original which contains highlighting over regions defined by the coordinate and size data. This overlaid image may be made semi-transparent to show the text in the region in the original image-based document through the highlight. Additionally, the duplicate image may be resized and translated to any position on a display as needed to show the highlighting to a user.

Document processor 306 provides the capabilities for identifying and showing the relevant portions of text within the image-based document used by several features disclosed herein, although one skilled in the relevant arts will appreciate that other applications where text is identified within an image may be used.

The text generated by OCR (such as by image to data component 306 a) lacks positional information within the image-based document itself. Computer vision component 306 b may look for shapes of characters or sequences of characters within the document to identify a position in the original images of the image-based document for text from the text-based version of the enforcement document. By way of non-limiting example, words, characters, or even sentences of the text-based enforcement document may be matched to pixel positions of the image-based enforcement document using computer vision component 306 b. Computer vision component 306 b may look for expected heights and widths of characters within the image-based enforcement document in order to identify these pixel positions, although one skilled in the relevant arts will appreciate that other computer vision approaches may be used.

Find text component 306 c is configured to obtain the pixel position information within the image-based document for given text, using the computer vision data from computer vision component 306 b. And, knowing the pixel position information for this text, highlight border component 306 d is able to render a highlight of the text. In accordance with an embodiment, highlight border component 306 d overlays a highlight on a layer appearing above the enforcement document. In an additional embodiment, highlight border component 306 d takes an image snippet from the original image-based document, inserts a highlight or highlight border in the appropriate pixel positions, and provides the image snippet for display to the user (typically in a separate panel or window from the original enforcement document).

FIG. 4 illustrates an exemplary image-based document highlight implementation 400, in accordance with an embodiment. An original, unaltered image-based document, such as an image-based PDF, may be shown in panel 402. This document may be streamed directly into a container for display in panel 402 from an original source, or may otherwise be unmodifiable to guarantee the originality of the document. In order to show text of interest in the document in panel 402, panel 404 is used to show a side-by-side image highlighting relevant text from the document, in an embodiment. One skilled in the relevant arts will appreciate that the precise positioning, sizing, and other features of the arrangement of panels 402 and 404 may vary, and the arrangement shown in document highlight implementation 400 is exemplary and non-limiting.

In order to show matched text within the image-based document, a copy of a current page of the image-based document is made, and document processor 306 of FIG. 3 performs computer vision processing to match text from a text-based version of the image-based document (e.g., a version of the image-based document that has been processed through image to data component 306 a) to a region of pixels in the copy of the image-based document. A highlight can be inserted into the region of pixels in the copy of the image-based document, as shown in panel 404, to direct a reader's view to the corresponding position of the original, unaltered image-based document displayed in panel 402.

FIG. 5 is an exemplary relationship structure 500 for associating citations in enforcement documents with regulatory requirements and mandates, in accordance with an embodiment. Relationship structure 500 may be implemented as a data model, usable to associate citation details 502 with regulatory requirements 504 and mandates 506 in, by way of non-limiting example, a relational database. As shown in the data model, there is an n-to-n relationship between citation details 502 and requirements 504, as well as between requirements 504 and mandates 506.

Mandates and requirements are understood, in part, by this relationship structure 500. Mandates 506 are a set of requirements 504. For example, a mandate may be a statutory law (e.g., the Fair Credit Reporting Act) that, in its implementation, is enforced through requirements. A mandate may also be an agency, such as the Office of Foreign Assets Control (OFAC), which imposes requirements. Requirements 504 may appear in more than one mandate 506, hence the n-to-n relationship. Requirements 504 are the specific obligations that an entity, such as a bank, that is subject to the mandates 506 must adhere to in order to remain compliant with the mandates 506.

Data following relationship structure 500 may be populated in a database in a number of different ways, including manually associating a citation with requirements, and requirements with mandates. In accordance with an embodiment, these relationships may also be established in accordance with document classification approaches discussed in co-pending U.S. patent application Ser. No. 16/536,645, entitled “SEARCH PATTERN SUGGESTIONS FOR LARGE DATASETS”, incorporated herein by reference in its entirety. For example, using the document classification approaches, it is possible to associate a new regulatory citation (previously not identified, and not associated with any requirements 504 in the data model) with a requirement 504 on the basis of other relationships appearing in the regulatory document.

Given relationship structure 500, it is possible to understand that citation details 502 appearing in an enforcement document indicate that the enforcement document relates to one or more requirements 504 associated with the citation details 502 (and, in turn, to one or more mandates 506 associated with each of the one or more requirements 504).

One of the advantages of the approach illustrated in FIGS. 1-5 is that enforcement documents are quickly and automatically scraped, and relevant enforcement documents provided to a user. These enforcement documents may be determined to be relevant because they include citation details 502 that relate to a mandate 506 (via requirements 504) that applies to a particular line of business that the user engages in or is interested in.

But while identifying relevant enforcement documents streamlines review from millions of pages of regulatory text to only those most important documents, embodiments disclosed herein also allow a user to directly identify relevant parts of an enforcement document. This is the case even if the enforcement document is an unmodified image-based document.

FIG. 6 illustrates an exemplary user interface 600 (UI 600), in accordance with an embodiment. UI 600 may include several controls for selecting a view of an enforcement document, such as a details view 602 and a PDF viewer 604, for example. In the details view, which is selected in UI 600, information regarding the enforcement document is shown in panel 612. This information includes, by way of non-limiting example, a summary, such as a summary provided by summarization component 304 b of FIG. 3 or summarization system 1120 of FIG. 11.

Metadata panel 606 includes extracted information about an enforcement action included in the enforcement document, in accordance with an embodiment. This information may be obtained by enforcement processor 304 of FIG. 3 by, for example, phrase extraction performed by phrase extraction component 304 b. Additionally, this information may be included in metadata available directly from the source of the enforcement document. One skilled in the relevant arts will appreciate that other approaches for populating this information are possible.

Relevant documents panel 608 includes links to other enforcement documents that share a relationship to the instant enforcement document selected in UI 600. This relationship may be established by extension from citation details 502 of FIG. 5. In accordance with an embodiment, a data model implementing relationship structure 500 may be extended to include enforcement documents. In an embodiment, this relationship is an n-to-n relationship between citation details 502 and enforcement documents. As a result, relevant documents panel 608 may show any enforcement documents that share common citation details 502. Filters may be applied to limit the number of relevant documents shown in relevant documents panel 608, such as limiting the relevant documents to those that share the most citations in common. One skilled in the relevant arts will appreciate that other implementations based on the relationship structure 500 are possible, and the foregoing examples are not limiting.

Mandates panel 610 allows a user of UI 600 to navigate to mandates, requirements, and individual citations within an enforcement document that are implicated in the enforcement document. In illustrative mandates panel 610, the mandates are collapsed into individual lines of business. In accordance with an embodiment, lines of business regulated by a particular mandate are listed in mandates panel 610 whenever the particular mandate is implicated by a requirement in the enforcement document.

In some embodiments, these individual lines of business in mandates panel 610 also tie to one or more policies that the business has put in place to be responsive to the mandates, requirements, and individual citations. In some embodiments, links to or details describing these policies, such as which mandate, requirement, or citation the policy is responsive to or information on what the policy requires the business to do, are included.

FIG. 7 illustrates an exemplary UI 700 with relevant information highlights, in accordance with an embodiment. In UI 700, PDF viewer control 704 is selected (and details control 702 is available). Similar panels 706, 708, and 710 are present, corresponding to panels 606, 608, and 610 of FIG. 6 respectively. However, in UI 700 the “risk management” line of business has been expanded to show a related mandate (OFAC) and related requirements under the mandate (risk-based screening and holding blocked property interest account) that are found in the enforcement document, as shown in panel 714. Selecting a requirement from this list highlights the relevant citations in document view 712. Selecting a mandate from this list highlights the relevant citations for all related requirements in document view 712.

In accordance with an embodiment, document view 712 shows an image-based version of the enforcement document. This enforcement document is original, and highlights are shown overlaid on the document. As noted above with regard to components 306 b, 306 c, and 306 d of document processor 306 in FIG. 3, the citations related to a mandate or requirement are shown in the image-based version of the enforcement document using the approaches described therein.

Given the relationships established in the relationship structure 500 of FIG. 5, it is possible to navigate through enforcement information in a number of ways to rapidly access relevant information. For example, given the relationship structure 500, it is possible to rapidly identify and navigate to documents related to an enforcement document currently being viewed. When an enforcement document is being viewed, it is possible to select a requirement of a mandate that is found within the enforcement document, made possible because of the enforcement-mandates-requirements relationship established in relationship structure 500. FIG. 8 is a flowchart 800 illustrating a navigation flow, in accordance with an embodiment. FIG. 8 is discussed in context with reference to UI 700 of FIG. 7. The process begins at step 802 where a line of business is selected. For example, in panel 710 of FIG. 7, a line of business named “risk management” can be expanded to show related mandates, which in turn can be expanded to show related requirements and policies under the mandate, as shown in panel 714.

At step 804, a relevant document is retrieved based on a selected requirement associated with the line of business at step 804. For example, when panel 710 is expanded by selecting a line of business, mandates related to that line of business are shown. Relationship structure 500 of FIG. 5 allows for the determination of requirements 504 related to a given mandate 506, such that panel 710 can be populated with a list of requirements for the mandates related to the selected line of business. If a requirement 504 is selected from panel 710, a list of relevant enforcement documents can be shown, per step 804. This is accomplished by identifying citation details 502 related to the selected requirement—any enforcement documents linked to citation details 502 can be retrieved. In accordance with an embodiment, the list of relevant enforcement documents can be shown in panel 708.

An enforcement document can be selected from the retrieved enforcement documents, in accordance with an embodiment. Since this enforcement document was accessed by first selecting a requirement 504, the enforcement document can be shown at step 806 with citations associated with the requirement highlighted in advance. In this way, it is possible to quickly navigate through various enforcement documents that implicate a specific requirement of a mandate (the mandate, optionally, selected via a line of business), while having the sections of the enforcement document related to that specific requirement highlighted.

Another navigation approach made possible by relationship structure 500 of FIG. 5 is the ability to quickly navigate to and view requirement information within a single enforcement document. FIG. 9 is a flowchart 900 illustrating an alternate navigation flow, in accordance with an embodiment. FIG. 9 is discussed in context with reference to UI 700 of FIG. 7. The process begins at step 902 where a mandate is selected (listed under a line of business), and a relevant document is retrieved at step 904 based on a requirement associated with the mandate.

At step 906, OCR is performed on the relevant document to generate a raw text version of the relevant document, and at step 908 the raw text is searched and highlighted for the text that is associated with the requirement. In addition, as described above, the unaltered original document can be shown (with or without its own highlighting) next to the raw text to allow a user to confirm accuracy. If a user of UI 700 were to select a different requirement, or navigate to a different mandate and select a requirement under that different mandate, the relevant sections of the relevant document can be immediately highlighted.

In some embodiments, the extraction of citations from documents, the classification of those documents, and the presentation of information relating to the documents and the citations is expanded to relate back to various aspects of a business, including the line of business. The requirements and other documents identified by the citations in the documents often drive businesses to establish policies that are responsive to those requirements. Regulatory documents that include citations to requirements can, in some cases, be a warning to a business to establish those policies. However, in other cases, the business has already established such policies. In either event, the disclosed embodiments enable a business or line of business to map its internal policies directly to regulatory citations and requirements to enable continuous monitoring of internal policies and their compliance with the regulatory requirements.

Existing systems and approaches rely on outside vendors to manually extract the citations. The above-described systems allow a computer to automatically extract the citations and then present the citations, the documents, and the related requirements for user review. In some embodiments described herein, the citations are further compared to the policies, allowing for identification of potential risks to the business based not only on the regulatory documents, but on the actual policies established by the business.

Mapping citations from regulatory documents to internal policies can be complicated by various aspects of the matching. For example, if two citations to the are to a statute and begin with different titles of that statute, such as “11 U.S.C.” and “10 U.S.C.”, the relative difference between the citations is small, but the difference in relevance is likely large. Other complications include when a citation in a regulatory document cites to a section of a statute, but the citation in the regulatory document cites to a subsection of that section. Simply comparing the two citations shows a difference, but even when a match is identified up to the section level, the implications of citing to the subsection versus the section may affect the importance of the match. A system for mapping or identifying the relationship between a citation in a regulatory document and a policy often needs to be able to identify the relevance of the relationship based on the level of match and what portions of the citation match.

By providing such a mapping, a business can respond to potential issues identified in regulatory documents. Additionally, the systems and methods face computer-specific technical problems that require technical solutions not necessary in existing manual methods. For example, OCR of documents can create errors or artifacts in the text. Those skilled in the art will appreciate that various methods and systems disclosed herein include specific technical solutions to these problems, such as error detection, as well as for other problems. Additionally, in many cases, the specific technical problems require more steps, which are computer-specific, than would be required for a person to manually perform the task.

FIG. 11 illustrates a block diagram of a system 1100 for analyzing documents, according to some embodiments. Document scraper 202 scrapes the documents from a regulatory website 1130 or another source, such as document source 1135. Document processing system 1110 processes documents, extracts information, and compares the documents to policies stored in policy database 1150. Document processing system 1110 can present results to a user on user interface 1160. Cloud 1170 is a network or cloud that allows for communication between various elements of system 1100. Some or all of the components in system 1100 can be implemented on one or more computer systems 1000, described above in FIG. 10.

As described above, documents, such as regulatory documents, are available on websites, such as regulatory website 1130, or from other sources, such as document source 1135. These documents contain various information that can be of interest. Scraper 202 scrapes these documents from the sources and scrapes or extracts text from them for analysis, as described in various embodiments above. Scraper 202 sends the text to document processing system 1110.

Document processing system 1110 may include citation extractor 1112, information extractor 1114, document classifier 1116, impact calculator 1118, and summarization system 1120. In some embodiments, citation extractor 1112 and document classifier 1116 perform the functions described above for FIG. 3 in various elements, such as pattern recognition 304 a and document tagging 304 d. In some embodiments, those or other functions are more particularly performed as described below in regards to FIG. 11.

Citation extractor 1112 searches text for citations and extracts them, such as described above in operation 104 of FIG. 1 above. In some embodiments, the document is searched using a citation key that identifies a citation or a portion of a citation. For example, a character string “C.F.R.” or “U.S.C.” can be used to identify citations to a statute. Other examples include references or abbreviations to court reporters, regulatory agencies, government agency adjudicatory bodies, such as the Patent Trial and Appeal Board (P.T.A.B), and other portions of a citation that are common to citations from a given organization or body. In some embodiments, citation extractor 1112 uses different citation keys, each citation key selected to match a different citation type. In some embodiments, citation extractor 1112 uses regular expressions to search the text and match the citation key.

When citation extractor 1112 identifies a match of the citation key in the text, it identifies it as a citation and extracts the citation. Citation extractor 1112 extracts the portion of the text matching the citation key. In some embodiments, citation extractor also extracts other text around the portion of the text matching the citation key. For example, a string of characters in the text immediately before or after the portion of the text matching the citation key is extracted as the citation.

In some embodiments, the other text is selected based on an expected citation formation. For example, the citation key matches a statutory citation element and the other text is a string of characters on either or both sides of the text matching the citation key that is sized based on the format of the statutory citation. In some embodiments, citation extractor selects the other text by searching the text for an indicator of an end of the citation, such as the beginning or the end. In some embodiments, the indicator is a stop character, such as a punctuation mark, a space, or characters or a string indicating a word in the text outside of the citation.

In some embodiments, citation extractor 1112 parses an extracted citation into different parts or sub-citations. The parsing can be performed based on expected punctuation or spaces between elements of a citation. For example, if a citation consists of a root, a section, and a subsection, the citation is parsed into sub-citations containing the root, the section, and the subsection, respectively. As a specific example, the citation “12 U.S.C. 1001.15(d)” has a root of “15” and a subsection of “d.” Note that other labels or naming conventions are possible, depending on the type of citation, as will be appreciated by those skilled in the art. For example, the “12” in the example can be a title sub-citation and the “1001” can be a section sub-citation.

In some embodiments, an extracted citation is evaluated to determine whether it includes a range or group of letters, numbers, or roman numerals. Citations use ranges to simultaneously cite to more than one reference at a time. When such a range is detected, citation extractor 1112 breaks the range up to identify all the different citations to include. For example, a citation citing to subsections a-c is broken into three citations, one for each subjection (a, b, and c). As another example, a citation citing to subsections i and iv (or i, iv) is broken into two citations, one for each of the two subsections.

In some embodiments, citation extractor 1112 flags or marks potential errors in citation matching for review. The identification of such citation extraction errors can be based on a metric of similarity or difference being greater than a threshold. For example, a string of characters closely matches, but is not exactly the same as the citation key. A distance metric can be used to measure differences in characters between the citation key and a string of characters in the text. When the difference is, as examples, a single character, a missing punctuation mark, or single character that is replaced with a different character that is commonly mistaken for the character in the citation key, the string is marked as a potential error. In this way, citation extractor 1112 is able to account for potential OCR errors that might otherwise cause a citation to be missed.

In some embodiments, citation extractor 1112 checks whether the extracted citation is a false positive. Regulatory documents include a variety of citations, not all of which are related to regulatory actions. For example, citations in documents are also for general citations to information or other documents or procedural citations that carry no punishment. Citation extractor identifies words or contextual clues using machine learning, natural language processing, or other techniques to differentiate citations. In some embodiments, when a false positive is detected, citation extractor 1112 ignores the citation or flags it for review.

In some embodiments, citation extractor 1112 identifies contextual clues to correct potential errors in a citation. For example, text in the document describes a violation of subsection (d) (i), but the subsection or the romanette is omitted from the citation or listed as a different section, such as (d)(ii). In some embodiments, the contextual clues related to these citations are used to correct the citation. In some embodiments, the contextual clues are used to flag the citation for manual review.

Information extractor 1114 extracts other information from the text. For example, information extractor 1114 extracts fine amounts, information about a topic of the document, such as a regulatory action, information about the citation, such as quoted section language, and other information. In some embodiments, the text is extracted using machine learning, natural language processing, or other techniques. In some embodiments, information extractor 1114 formats the information extracted as well.

As an example, a group of fine amounts is identified based on surrounding text or symbols, such as text discussing a fine or a dollar sign. The corresponding fine amounts are extracted. The group of fine amounts are analyzed to determine if any duplicate amounts are present, such as by detecting duplicate values. The duplicate values are deleted or removed from the group of fine amounts and the remaining fine amounts are totaled to identify a total fine amount.

In another example, a fine amount is identified and extracted. The fine amount is made up of a number, such as “11,” and a word defining an amount, such as “million.” The fine amount is converted to a numerical amount, such as by multiplying the number by a number amount represented by the word, in this example 1,000,000. The numerical amount is then used as the fine amount.

Document classifier 1116 classifies the document from which the text was scraped. In some embodiments, the classification is based on a citation extracted from the document and a policy for a business that is responsive to a requirement that the citation cites to. For example, if a document contains a citation to a statutory requirement and the business has a policy for that statutory requirement, the document is classified based on a level of matching between the citation in the document and the citation in the policy. In some embodiments, the classification is based on the sub-citations in the citation from the regulatory document and the sub-citations in the in the policy. In some embodiments, document classifier 1116 compares the citation from the document to the citation from the policy character by character, starting at the beginning of the string.

Policy database 1150 stores policies for a business. in some embodiments, a policy includes a citation to a reference, such as a statute, that is the source of the policy. The policy includes information describing how the business is responsive to the reference. For example, a business handling toxic waste disposal has a policy responsive to a statute detailing safety standards for toxic waste. The relevant statute is one of the citations. In some embodiments, the policy has details on risk factors or importance to the business.

In some embodiments, document classifier 1116 compares the citation and requirements or mandates corresponding to the citation to the contents of the policy. Techniques such as natural language processing or machine learning are used to identify a relationship between the citation and the policy. Document classifier 1116 determines a classification for the document that the citation is from based on the relationship. For example, the policy and the requirements corresponding to the citation discuss the same subject matter, as determined by a natural language processing algorithm, and document classifier 1116 classifies the document as related to the policy. As another example, the policy quotes some of the requirements corresponding to the citation and document classifier 1116 classifies the document as related to the policy.

In some embodiments, document classifier 1116 compares sub-citations from a citation in a document to sub-citations from a citation in a policy. In some embodiments, document classifier 1116 performs the classification based on a level of matching between the sub-citations in a first citation extracted from the document and sub-citations in a second citation in the policy. When the first citation and the second citation are the same (meaning that the sub-citations in both are the same) then the document is classified as being an exact match for the policy. When the first citation and the second citation both have matching roots and subsections, the document is classified as being a subsection match for the policy. When the roots for the first and second citations match, but the subsection is different, the document is classified as a root match for the policy, and when the root does not match, the document is classified as a non-match for the policy. In some embodiments, prior to classifying the document based on these rules, the title and section of the first and second citation are first compared and, when one or both are different, the document is classified as a non-match for the policy.

In some embodiments, document classifier 1116 uses a similarity score to evaluate the match between the citation in the document and the citation in the policy. In some embodiments, the similarity score is in addition to the other methods of comparison in the classification described herein. Due to the nature of citations, some metrics may identify two citations as similar when, in fact, they are quite different. This can depend on the exact structure of the citation. For example, 10 U.S.C. 100.5(d) is very different from 11 U.S.C. 100.5(d), but they only differ by one character. Those skilled in the art will understand that the technique used to determine the similarity score can account for these problems. In some embodiments, the similarity score is configured to weight the characters in the citation based on their position in the citation. For example, Jaro-Winkler distance weights the score so that two strings that match from the beginning have a higher similarity.

Impact calculator 1118 determines a score describing an impact of the document on a business. The score can be based on the policy, the citation, the classification of the document, text in the document relating to the citation, or a fine amount corresponding to the citation. The score is determined as a metric indicating how important the document is to a business given a policy. For example, if the document is classified as an exact match to the policy, but there is no punishment or action needed, the score is low. When the document is not a match to the policy, the score may be low. When the document is an exact match and the fine amount is large, the score may be high or higher. In some embodiments, when the citation in the policy is broad, such as only to a section or root level, but the citation from the document is more specific, the score may be low. When the citation in the policy is specific and the citation in the document is broader, the value may be medium.

In some embodiments, the score is calculated in such a way to determine risk to the business based on existing policies in the business and regulatory actions reflected in the regulatory documents. As an example, if regulatory documents indicate that certain statutes apply to the business based on the presence of a citation, and that citation relates to a policy in the business that is not effective, not in place, or still being developed, the score may be set to a value that is higher to indicate potential risk. This allows impact calculator 1118 to flag the document for review and ensure that the business is responsive to the potential risk to the business, its customers, or others.

Summarization system 1120 prepares and presents a report or summary detailing an impact or risk of a given document to the business. In some embodiments, the report is only prepared when the score is above a threshold. User interface 1160 presents the report to a user, such as through a website, email, or automated system message. In some embodiments, user interface 1160 presents the summary using embodiments of a UI, such as those described in FIGS. 6 and 7.

In some embodiments, the summary includes the score, a link to the document, a link to the policy, the citation, a link to a reference document described by the citation, the classification of the document, a total fine amount relating to the citation, or other information. The summary is configured to identify that a policy and a document are related and have potential to impact the business. A user is able to use the summary to access the policy, the document, the relevant citation, and review information relating to why the document was flagged. In this way, document processing system 1100 generates a report flagging potential business concerns based on scraped documents and their relevance to existing policies.

FIG. 12 is a flowchart illustrating a method 1200 for processing documents, according to some embodiments. In some embodiments, the documents are regulatory documents. In some embodiments, processing the documents involves extracting citations, comparing them to policies for a business, and presenting the results of the processing.

In 1210, document scraper 202 scrapes a document from a source. In some embodiments, the document is a regulatory document. In some embodiments, the source is regulatory website 1130 or document source 1135. Document scraper 202 scrapes the document from the website. In some embodiments, document scraper 202 extracts text from the document. For example, document scraper 202 performs OCR to extract text from the document in a searchable format.

In 1220, citation extractor 1112 extracts a citation from the document. In some embodiments, the citation is a citation relating to a regulatory violation described in the document. In some embodiments, the citation is extracted based on a citation key. In some embodiments, citation extractor 1112 searches the text of the document using a regular expression. In some embodiments, citation extractor 1112 uses different citation keys, each citation key selected to match a different citation type.

In some embodiments, citation extractor 1112 extracts a portion of the text matching the citation key. In some embodiments, citation extractor also extracts other text around the portion of the text matching the citation key. In some embodiments, the other text is selected based on an expected citation formation. In some embodiments, citation extractor selects the other text by searching the text for an indicator of an end of the citation, such as the beginning or the end.

In some embodiments, operation 1220 includes parsing an extracted citation into different parts or sub-citations. The parsing can be performed based on expected punctuation or spaces between elements of a citation.

In some embodiments, operation 1220 includes the extracted citation being evaluated to determine whether it includes a range or group of letters, numbers, or roman numerals.

In some embodiments, operation 1220 flags or marks potential errors in citation matching for review. In some embodiments, a distance metric is used to measure differences in characters between the citation key and a string of characters in the text.

In some embodiments, operation 1220 includes checking whether the extracted citation is a false positive. In some embodiments, when a false positive is detected, citation extractor 1112 ignores the citation or flags it for review.

In 1230, information extractor 1114 extracts a fine amount corresponding to the citation. The fine amounts correspond to the citation because the fines are the result of a violation of a regulation or requirement that is referenced by the citation. In some embodiments, the fine amount is a combination of more than one fine amount related to the citation. In some embodiments, information extractor 1114 formats the information extracted as well. In some embodiments, a group of fine amounts is analyzed to determine if any duplicate amounts are present, such as by detecting duplicate values. The duplicate values are deleted or removed from the group of fine amounts and the remaining fine amounts are totaled to identify a total fine amount.

In some embodiments, operation 1230 includes converting a fine mount made up of a number, such as “11,” and a word defining an amount, such as “million,” into a numerical value. The fine amount is converted to a numerical amount, such as by multiplying the number by a number amount represented by the word, in this example 1,000,000. The numerical amount is then used as the fine amount.

In 1240, document classifier 1116 classifies the document based on the citation and a policy. In some embodiments, the policy is for a business that is responsive to a requirement that the citation cites to. In some embodiments, the classification is based on the sub-citations in the citation from the regulatory document and the sub-citations in the policy. In some embodiments, document classifier 1116 compares the citation from the document to the citation from the policy character by character, starting at the beginning of the string.

In some embodiments, document classifier 1116 accesses policy database 1150 to compare the citation to policy citations in the policies. In some embodiments, citations are extracted from the policies. In some embodiments, the citations are extracted using citation extractor 1112.

In some embodiments, operation 1240 performs the classification based on a level of matching between the sub-citations in a first citation extracted from the document and sub-citations in a second citation in the policy. When the first citation and the second citation are the same, that is, the sub-citations in both are the same, then the document is classified as being an exact match for the policy. When the first citation and the second citation both have matching roots and subsections, the document is classified as being a subsection match for the policy. When the roots for the first and second citations match, but the subsection is different, the document is classified as a root match for the policy, and when the root does not match, the document is classified as a non-match for the policy. In some embodiments, prior to classifying the document based on these rules, the title and section of the first and second citation are first compared and, when one or both are different, the document is classified as a non-match for the policy.

In some embodiments, document classifier 1116 uses a similarity score to evaluate the match between the citation in the document and the citation in the policy. In some embodiments, the similarity score is in addition to the other methods of comparison in the classification described herein.

In 1250, impact calculator 1118 determines an impact score of the document. The impact score is a score describing an impact of the document on a business. In some embodiments, the score is based on the policy, the citation, the classification of the document, text in the document relating to the citation, or a fine amount corresponding to the citation. In some embodiments, the score is calculated in such a way to determine risk to the business based on existing policies in the business and regulatory actions reflected in the regulatory documents.

In 1260, user interface 1160 presents a summary of the citation. In some embodiments, summarization system 1120 prepares the summary detailing an impact or risk of a given document to the business. In some embodiments, the report is only prepared when the score is above a threshold. User interface 1160 presents the report to a user, such as through a website, email, or automated system message. In some embodiments, user interface 1160 presents the summary using embodiments of a UI, such as those described in FIGS. 6 and 7.

In some embodiments, the summary includes the score, a link to the document, a link to the policy, the citation, a link to a reference document described by the citation, the classification of the document, a total fine amount relating to the citation, or other information.

Various embodiments may be implemented, for example, using one or more well-known computer systems, such as computer system 1000 shown in FIG. 10. One or more computer systems 1000 may be used, for example, to implement any of the embodiments discussed herein, as well as combinations and sub-combinations thereof.

Computer system 1000 may include one or more processors (also called central processing units, or CPUs), such as a processor 1004. Processor 1004 may be connected to a communication infrastructure or bus 1006.

Computer system 1000 may also include user input/output device(s) 1003, such as monitors, keyboards, pointing devices, etc., which may communicate with communication infrastructure 1006 through user input/output interface(s) 1002.

One or more of processors 1004 may be a graphics processing unit (GPU). In an embodiment, a GPU may be a processor that is a specialized electronic circuit designed to process mathematically intensive applications. The GPU may have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common to computer graphics applications, images, videos, etc.

Computer system 1000 may also include a main or primary memory 1008, such as random access memory (RAM). Main memory 1008 may include one or more levels of cache. Main memory 1008 may have stored therein control logic (i.e., computer software) and/or data.

Computer system 1000 may also include one or more secondary storage devices or memory 1010. Secondary memory 1010 may include, for example, a hard disk drive 1012 and/or a removable storage device or drive 1014. Removable storage drive 1014 may be a floppy disk drive, a magnetic tape drive, a compact disk drive, an optical storage device, tape backup device, and/or any other storage device/drive.

Removable storage drive 1014 may interact with a removable storage unit 1018. Removable storage unit 1018 may include a computer usable or readable storage device having stored thereon computer software (control logic) and/or data. Removable storage unit 1018 may be a floppy disk, magnetic tape, compact disk, DVD, optical storage disk, and/ any other computer data storage device. Removable storage drive 1014 may read from and/or write to removable storage unit 1018.

Secondary memory 1010 may include other means, devices, components, instrumentalities or other approaches for allowing computer programs and/or other instructions and/or data to be accessed by computer system 1000. Such means, devices, components, instrumentalities or other approaches may include, for example, a removable storage unit 1022 and an interface 1020. Examples of the removable storage unit 1022 and the interface 1020 may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and/or any other removable storage unit and associated interface.

Computer system 1000 may further include a communication or network interface 1024. Communication interface 1024 may enable computer system 1000 to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively referenced by reference number 1028). For example, communication interface 1024 may allow computer system 1000 to communicate with external or remote devices 1028 over communications path 1026, which may be wired and/or wireless (or a combination thereof), and which may include any combination of LANs, WANs, the Internet, etc. Control logic and/or data may be transmitted to and from computer system 1000 via communication path 1026.

Computer system 1000 may also be any of a personal digital assistant (PDA), desktop workstation, laptop or notebook computer, netbook, tablet, smart phone, smart watch or other wearable, appliance, part of the Internet-of-Things, and/or embedded system, to name a few non-limiting examples, or any combination thereof.

Computer system 1000 may be a client or server, accessing or hosting any applications and/or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; local or on-premises software (“on-premise” cloud-based solutions); “as a service” models (e.g., content as a service (CaaS), digital content as a service (DCaaS), software as a service (SaaS), managed software as a service (MSaaS), platform as a service (PaaS), desktop as a service (DaaS), framework as a service (FaaS), backend as a service (BaaS), mobile backend as a service (MBaaS), infrastructure as a service (IaaS), etc.); and/or a hybrid model including any combination of the foregoing examples or other services or delivery paradigms.

Any applicable data structures, file formats, and schemas in computer system 1000 may be derived from standards including but not limited to JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or any other functionally similar representations alone or in combination. Alternatively, proprietary data structures, formats or schemas may be used, either exclusively or in combination with known or open standards.

In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer useable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 1000, main memory 1008, secondary memory 1010, and removable storage units 1018 and 1022, as well as tangible articles of manufacture embodying any combination of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system 1000), may cause such data processing devices to operate as described herein.

Based on the teachings contained in this disclosure, it will be apparent to persons skilled in the relevant art(s) how to make and use embodiments of this disclosure using data processing devices, computer systems and/or computer architectures other than that shown in FIG. 10. In particular, embodiments can operate with software, hardware, and/or operating system implementations other than those described herein.

It is to be appreciated that the Detailed Description section, and not any other section, is intended to be used to interpret the claims. Other sections can set forth one or more but not all exemplary embodiments as contemplated by the inventor(s), and thus, are not intended to limit this disclosure or the appended claims in any way.

While this disclosure describes exemplary embodiments for exemplary fields and applications, it should be understood that the disclosure is not limited thereto. Other embodiments and modifications thereto are possible, and are within the scope and spirit of this disclosure. For example, and without limiting the generality of this paragraph, embodiments are not limited to the software, hardware, firmware, and/or entities illustrated in the figures and/or described herein. Further, embodiments (whether or not explicitly described herein) have significant utility to fields and applications beyond the examples described herein.

Embodiments have been described herein with the aid of functional building blocks illustrating the implementation of specified functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternate boundaries can be defined as long as the specified functions and relationships (or equivalents thereof) are appropriately performed. Also, alternative embodiments can perform functional blocks, steps, operations, methods, etc. using orderings different than those described herein.

References herein to “one embodiment,” “an embodiment,” “an example embodiment,” or similar phrases, indicate that the embodiment described can include a particular feature, structure, or characteristic, but every embodiment can not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it would be within the knowledge of persons skilled in the relevant art(s) to incorporate such feature, structure, or characteristic into other embodiments whether or not explicitly mentioned or described herein. Additionally, some embodiments can be described using the expression “coupled” and “connected” along with their derivatives. These terms are not necessarily intended as synonyms for each other. For example, some embodiments can be described using the terms “connected” and/or “coupled” to indicate that two or more elements are in direct physical or electrical contact with each other. The term “coupled,” however, can also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other.

The breadth and scope of this disclosure should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents. 

What is claimed is:
 1. A method, comprising: extracting, by at least one processor, a citation from a document; parsing, by the at least one processor, the citation into first one or more sub-citations; comparing, by the at least one processor, the first one or more sub-citations to second one or more sub-citations of a reference citation in a policy; and classifying, by the at least one processor, the document as relevant to the policy based on the first one or more sub-citations and the second one or more sub-citations.
 2. The method of claim 1, wherein extracting the citation from the document comprises: comparing text in the document to a citation key; and in response to identifying a first string of characters from the text that matches the citation key, extracting a second string of characters comprising the first string of characters, a third string of characters located in the text immediately before the first string and a fourth string of characters located in the text immediately after the first string.
 3. The method of claim 1, wherein extracting the citation from the document comprises: comparing text in the document to a citation key, wherein the citation key comprises a first string of characters; and in response to identifying a second string of characters from the text that differs from the citation key by at least one character and having a matching metric greater than a threshold, marking the second string of characters as a citation extraction error.
 4. The method of claim 1, wherein: the first one or more sub-citations comprises a first root and a first sub-section; the second one or more sub-citations comprises a second root and a second sub-section; and classifying the citation based on the first one or more sub-citations and the second one or more sub-citations comprises: in response to the first one or more sub-citations matching the second one or more sub-citations, classifying the document as an exact match for the policy; in response to the first root matching the second root and the first subsection matching the second subsection, classifying the document as a subsection match for the policy; in response to the first root matching the second root and the first subsection being different than the second subsection, classifying the document as a root match for the policy; and in response to the first root being different than the second root, classifying the document as a non-match for the policy.
 5. The method of claim 1, further comprising: extracting, by the at least one processor, one or more fine amounts from the document, wherein the one or more fine values correspond to the citation; removing, by the at least one processor, each duplicate fine amount from the one or more fine amounts to form a set of fine amounts; and combining, by the at least one processor, the set of fine amounts into a total fine amount corresponding to the citation.
 6. The method of claim 1, further comprising: extracting, by the at least one processor, a fine amount from the document, wherein the fine amount comprises a number prefix and a word suffix; and combining, by the at least one processor, the number prefix and the word suffix to form a number amount for the fine amount.
 7. The method of claim 1, further comprising: determining, by the at least one processor, a score describing an impact of the document on a business based on the policy, the citation, the classification of the document, and text in the document related to the citation; and in response to the score being greater than a threshold, presenting, by the at least one processor, a summary of the impact of the document on the business, wherein the summary comprises one or more of a link to the policy, the citation, a link to a reference document described by the citation, a classification of the document, a link to the document, or the score.
 8. The method of claim 7, further comprising: extracting, by the at least one processor, a total fine amount corresponding to the citation, wherein determining the score describing the impact of the document on the business is further based on the total fine amount; and wherein the summary further comprises the total fine amount.
 9. A system, comprising: one or more processors; memory communicatively coupled to the one or more processors, the memory storing instructions which, when executed by the one or more processors, cause the one or more processors to: extract a citation from a document; parse the citation into first one or more sub-citations; compare the first one or more sub-citations to second one or more sub-citations of a reference citation in a policy; and classify the document as relevant to the policy based on the first one or more sub-citations and the second one or more sub-citations.
 10. The system of claim 9, wherein the instructions are further configured to extract the citation from the document by: comparing text in the document to a citation key; and in response to identifying a first string of characters from the text that matches the citation key, extracting a second string of characters comprising the first string of characters, a third string of characters located in the text immediately before the first string and a fourth string of characters located in the text immediately after the first string.
 11. The system of claim 9, wherein the instructions are further configured to extract the citation from the document by: comparing text in the document to a citation key, wherein the citation key comprises a first string of characters; and in response to identifying a second string of characters from the text that differs from the citation key by at least one character and having a matching metric greater than a threshold, marking the second string of characters as a citation extraction error.
 12. The system of claim 9, wherein: the first one or more sub-citations comprises a first root and a first sub-section; the second one or more sub-citations comprises a second root and a second sub-section; and the instructions are further configured to classify the citation based on the first one or more sub-citations and the second one or more sub-citations by: in response to the first one or more sub-citations matching the second one or more sub-citations, classifying the document as an exact match for the policy; in response to the first root matching the second root and the first subsection matching the second subsection, classifying the document as a subsection match for the policy; in response to the first root matching the second root and the first subsection being different than the second subsection, classifying the document as a root match for the policy; and in response to the first root being different than the second root, classifying the document as a non-match for the policy.
 13. The system of claim 9, wherein the instructions are further configured to: extract one or more fine amounts from the document, wherein the one or more fine values correspond to the citation; remove each duplicate fine amount from the one or more fine amounts to form a set of fine amounts; and combine the set of fine amounts into a total fine amount corresponding to the citation.
 14. The system of claim 9, wherein the instructions are further configured to: extract a fine amount from the document, wherein the fine amount comprises a number prefix and a word suffix; and combine the number prefix and the word suffix to form a number amount for the fine amount.
 15. The system of claim 9, wherein the instructions are further configured to: determine a score describing an impact of the document on a business based on the policy, the citation, the classification of the document, and text in the document related to the citation; and in response to the score being greater than a threshold, present a summary of the impact of the document on the business, wherein the summary comprises one or more of a link to the policy, the citation, a link to a reference document described by the citation, a classification of the document, a link to the document, or the score.
 16. The system of claim 15, wherein the instructions are further configured to: extract a total fine amount corresponding to the citation, wherein determining the score describing the impact of the document on the business is further based on the total fine amount; and wherein the summary further comprises the total fine amount.
 17. A non-transitory computer readable storage medium having computer readable code thereon, the non-transitory computer readable storage medium including instructions configured to cause a computer system to perform operations, comprising: extracting a citation from a document; parsing the citation into first one or more sub-citations; comparing the first one or more sub-citations to second one or more sub-citations of a reference citation in a policy; and classifying the document as relevant to the policy based on the first one or more sub-citations and the second one or more sub-citations.
 18. The non-transitory computer readable storage medium of claim 17, wherein the operations extract the citation from the document by: comparing text in the document to a citation key; and in response to identifying a first string of characters from the text that matches the citation key, extracting a second string of characters comprising the first string of characters, a third string of characters located in the text immediately before the first string and a fourth string of characters located in the text immediately after the first string.
 19. The non-transitory computer readable storage medium of claim 17, wherein: the first one or more sub-citations comprises a first root and a first sub-section; the second one or more sub-citations comprises a second root and a second sub-section; and the operations classify the citation based on the first one or more sub-citations and the second one or more sub-citations by: in response to the first one or more sub-citations matching the second one or more sub-citations, classifying the document as an exact match for the policy; in response to the first root matching the second root and the first subsection matching the second subsection, classifying the document as a subsection match for the policy; in response to the first root matching the second root and the first subsection being different than the second subsection, classifying the document as a root match for the policy; and in response to the first root being different than the second root, classifying the document as a non-match for the policy.
 20. The non-transitory computer readable storage medium of claim 17, wherein the operations further comprise: determining, by the at least one processor, a score describing an impact of the document on a business based on the policy, the citation, the classification of the document, and text in the document related to the citation; and in response to the score being greater than a threshold, presenting, by the at least one processor, a summary of the impact of the document on the business, wherein the summary comprises one or more of a link to the policy, the citation, a link to a reference document described by the citation, a classification of the document, a link to the document, or the score. 